lntegration and Visualization of Diverse Biological Data

项目总结 大多数人类疾病的发生涉及到许多分子水平的变化，涉及到复杂的 在特定的细胞谱系、途径和治疗环境中功能不同的相互作用的基因和途径。 这一系统被数以千计的功能基因组学和数量遗传学研究所探索，并且是综合的 对这些数据的分析可以产生可检验的假说，识别因果遗传变异，并将它们与 细胞网络水平的变化对疾病表型的影响。这可以使人们在分子水平上更深入地了解 病理生理学，为基于基因组的精确医学铺平了道路。 该项目的长期目标是通过对高密度元素的综合分析来实现这些发现。 疾病背景下的吞吐量生物学数据。在之前的资助期间，我们开发了准确的数据 集成方法，创建了用于通过特定上下文和 机械网络模型和定量遗传学数据的分析，并对重要的 生物过程和疾病。我们通过建立公共的公共设施进一步促进了实验生物学的发现 在实验生物学家中很受欢迎的交互式系统，能够进行实时的用户驱动的集成。 我们现在建议将这些基因水平的功能网络方法与潜在的基因组联系起来 通过破译基因组变异如何导致特定的转录和转录后效应来实现变异。我们 建议开发能够预测任何基因组的生化效应的从头算序列水平模型 染色质状态和RNA调节的变异(包括罕见或从未观察到)，然后将这些影响与 基因水平的调控后果(包括组织特异性转录和RNA剪接)，最终将 基因组序列通过统计方法直接进入网络环境，用于检测基因和网络 疾病中突变负担显著增加的社区。我们的关键交付内容将是用户- 友好、交互式的基于Web的框架，支持在网络环境中进行系统级变化影响分析 以及一个面向计算科学家的开放源码库。除了跨上下文和 我们将与实验者合作，将我们的方法应用于阿尔茨海默氏症、自闭症谱系 疾病、慢性肾脏疾病、免疫疾病和先天性心脏病作为 反复改进我们的方法，并直接有助于更好地了解这些疾病。

PROJECT SUMMARY The onset of most human disease involves numerous molecular-level changes to the complex system of interacting genes and pathways that function differently in specific cell-lineage, pathway, and treatment contexts. This system is probed by thousands of functional genomics and quantitative genetic studies, and integrative analysis of these data can generate testable hypotheses identifying causal genetic variants and linking them to network level changes in cells to disease phenotypes. This can enable deeper molecular-level understanding of pathophysiology, paving the way to genome-based precision medicine. The long term goal of this project is to enable such discoveries through integrative analysis of high- throughput biological data in a disease context. In the previous funding periods, we developed accurate data integration methods, created algorithms for the prediction of disease genes through context-specific and mechanistic network models and analysis of quantitative genetics data, and made novel insights into important biological processes and diseases. We further enabled experimental biological discovery by building public interactive systems capable of real-time user-driven integration that are popular among experimental biologists. We now propose to connect these gene-level functional network approaches with the underlying genomic variation by deciphering how genomic variants lead to specific transcriptional and posttranscriptional effects. We propose to develop ab initio sequence-level models capable of predicting biochemical effects of any genomic variant (including rare or never observed) on chromatin state and RNA regulation, then link these effects with gene-level regulatory consequences (including tissue-specific transcription and RNA splicing), and finally put genomic sequence directly into the network context via a statistical approach for detecting genes and network neighborhoods with a significantly elevated mutational burden in disease. Our key deliverable will be a user- friendly, interactive web-based framework enabling systems-level variant impact analysis in a network context and an open source library for computational scientists. In addition to systematic analysis across contexts and diseases, we will collaborate with experimentalists to apply our methods to Alzheimer’s, autism spectrum disorders, chronic kidney disease, immune diseases, and congenital heart defects as case studies for the iterative improvement of our methods and to directly contribute to better understanding of these diseases.